25 citations · 61 across the 11 of their papers we have counts for
12 papers
Neuro-GPT: Towards A Foundation Model for EEG
Wenhui Cui, Woojae Jeong, Philipp Thölke +4
To handle the scarcity and heterogeneity of electroencephalography (EEG) data for Brain-Computer Interface (BCI) tasks, and to harness the power of large publicly available data se…
Toward Improved Generalization: Meta Transfer of Self-supervised Knowledge on Graphs
Wenhui Cui, Haleh Akrami, Anand A. Joshi +1
Despite the remarkable success achieved by graph convolutional networks for functional brain activity analysis, the heterogeneity of functional patterns and the scarcity of imaging…
Learning From Positive and Unlabeled Data Using Observer-GAN
Omar Zamzam, Haleh Akrami, Richard Leahy
The problem of learning from positive and unlabeled data (A.K.A. PU learning) has been studied in a binary (i.e., positive versus negative) classification setting, where the input…
Learning from imperfect training data using a robust loss function: application to brain image segmentation
Haleh Akrami, Wenhui Cui, Anand A Joshi +1
Segmentation is one of the most important tasks in MRI medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis…
Semi-supervised Learning using Robust Loss
Wenhui Cui, Haleh Akrami, Anand A. Joshi +1
The amount of manually labeled data is limited in medical applications, so semi-supervised learning and automatic labeling strategies can be an asset for training deep neural netwo…
fMRI-Kernel Regression: A Kernel-based Method for Pointwise Statistical Analysis of rs-fMRI for Population Studies
Anand A. Joshi, Soyoung Choi, Haleh Akrami +1
Due to the spontaneous nature of resting-state fMRI (rs-fMRI) signals, cross-subject comparison and therefore, group studies of rs-fMRI are challenging. Most existing group compari…
Realistic head modeling of electromagnetic brain activity: An integrated Brainstorm pipeline from MRI data to the FEM solution
Takfarinas Medani, Juan Garcia-Prieto, Francois Tadel +6
Human brain activity generates scalp potentials (electroencephalography EEG), intracranial potentials (iEEG), and external magnetic fields (magnetoencephalography MEG), all capable…
Addressing Variance Shrinkage in Variational Autoencoders using Quantile Regression
Haleh Akrami, Anand A. Joshi, Sergul Aydore +1
Estimation of uncertainty in deep learning models is of vital importance, especially in medical imaging, where reliance on inference without taking into account uncertainty could l…
Robust Variational Autoencoder for Tabular Data with Beta Divergence
Haleh Akrami, Sergul Aydore, Richard M. Leahy +1
We propose a robust variational autoencoder with divergence for tabular data (RTVAE) with mixed categorical and continuous features. Variational autoencoders (VAE) and their va…
3D Phase Retrieval at Nano-Scale via Accelerated Wirtinger Flow
Zalan Fabian, Justin Haldar, Richard Leahy +1
Imaging 3D nano-structures at very high resolution is crucial in a variety of scientific fields. However, due to fundamental limitations of light propagation we can only measure th…
Robust Variational Autoencoder
Haleh Akrami, Anand A. Joshi, Jian Li +2
Machine learning methods often need a large amount of labeled training data. Since the training data is assumed to be the ground truth, outliers can severely degrade learned repres…
Accelerated Wirtinger Flow: A fast algorithm for ptychography
Rui Xu, Mahdi Soltanolkotabi, Justin P. Haldar +4
This paper presents a new algorithm, Accelerated Wirtinger Flow (AWF), for ptychographic image reconstruction from phaseless diffraction pattern measurements. AWF is based on combi…